AI-based condition monitoring of a variable displacement axial piston pump

Abid Abdul Azeez, Elina Vuorinen, Tatiana Minav, Paolo Casoli

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

Abstract

Conventional condition monitoring involves integration of additional sensors for fault detection and diagnosis. They are costly and sensitive to faults themselves. To overcome these issues and data scarcity, simulation model data is used as a source of training data for Artificial Intelligence based condition monitoring of the axial piston pump. The sensitivity of the simulation model is improved by performing data augmentation. The classification of faults for condition monitoring in the model is performed by developing a classifier utilizing machine learning algorithm. This was tested for experimental, simulation, and augmented simulation data with respective accuracy scores of 84.8%, 70.1%, and 75.7%. Hence, augmented simulation data is a suitable option for online condition monitoring.
Original languageEnglish
Title of host publicationThe 13th International Fluid Power Conference
EditorsKatharina Schmitz
Place of PublicationAachen, Germany
Pages921-931
Publication statusPublished - Jun 2022
Publication typeD3 Professional conference proceedings
EventInternational Fluid Power Conference - Aachen, Germany
Duration: 13 Jun 202215 Jun 2022
Conference number: 13

Conference

ConferenceInternational Fluid Power Conference
Country/TerritoryGermany
CityAachen
Period13/06/2215/06/22

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